Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	135,321
2	South Dakota	133,112
3	Rhode Island	129,633
4	Utah	120,307
5	Tennessee	116,782
6	Arizona	115,653
7	Iowa	111,376
8	Oklahoma	110,782
9	Arkansas	109,482
10	Wisconsin	109,275
11	Nebraska	108,514
12	South Carolina	107,139
13	Alabama	105,112
14	Kansas	104,574
15	Mississippi	102,530
16	Indiana	102,460
17	New Jersey	102,318
18	Idaho	101,083
19	Nevada	98,634
20	Illinois	98,496
21	Montana	97,940
22	Georgia	97,498
23	Delaware	97,356
24	Wyoming	97,294
25	Texas	96,361
26	Kentucky	96,272
27	New York	96,208
28	Florida	95,807
29	Louisiana	95,709
30	Missouri	95,351
31	California	92,854
32	Massachusetts	92,213
33	Minnesota	92,134
34	New Mexico	91,400
35	North Carolina	87,701
36	Connecticut	87,198
37	Ohio	87,052
38	Alaska	85,292
39	Colorado	80,709
40	Pennsylvania	80,488
41	West Virginia	79,088
42	Michigan	74,692
43	Virginia	72,517
44	Maryland	68,069
45	District of Columbia	63,071
46	New Hampshire	61,907
47	Washington	48,182
48	Puerto Rico	44,176
49	Oregon	39,123
50	Maine	37,571
51	Vermont	30,889
52	Hawaii	21,032

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	754
2	Connecticut	530
3	Rhode Island	488
4	New Jersey	456
5	New York	383
6	Pennsylvania	356
7	Alaska	296
8	Massachusetts	292
9	New Hampshire	281
10	North Carolina	278
11	Minnesota	264
12	Delaware	253
13	Vermont	243
14	Tennessee	230
15	Colorado	228
16	North Dakota	222
17	Florida	213
18	Idaho	211
19	West Virginia	209
20	South Dakota	194
21	Illinois	182
22	Maryland	181
23	Nebraska	180
24	Iowa	178
25	Ohio	169
26	Maine	162
27	District of Columbia	159
28	South Carolina	157
29	Montana	153
30	Wyoming	152
31	Washington	142
32	Virginia	140
33	Texas	139
34	Georgia	134
35	Louisiana	133
36	Kentucky	126
37	Indiana	122
38	Oklahoma	117
39	Utah	112
40	Wisconsin	110
41	Kansas	108
42	Puerto Rico	102
43	New Mexico	96
44	Missouri	91
45	Arizona	88
46	Oregon	83
47	Nevada	77
48	Alabama	67
49	California	66
50	Hawaii	59
51	Mississippi	59
52	Arkansas	51

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,761
2	New York	2,559
3	Massachusetts	2,493
4	Rhode Island	2,472
5	Mississippi	2,362
6	Arizona	2,331
7	Connecticut	2,211
8	South Dakota	2,187
9	Louisiana	2,181
10	Alabama	2,152
11	North Dakota	1,965
12	Pennsylvania	1,964
13	Indiana	1,936
14	New Mexico	1,878
15	Arkansas	1,864
16	Illinois	1,860
17	Iowa	1,820
18	South Carolina	1,776
19	Georgia	1,739
20	Tennessee	1,727
21	Michigan	1,713
22	Nevada	1,704
23	Kansas	1,686
24	Texas	1,673
25	Delaware	1,601
26	Ohio	1,591
27	Florida	1,556
28	District of Columbia	1,507
29	California	1,499
30	West Virginia	1,493
31	Missouri	1,462
32	Kentucky	1,376
33	Maryland	1,370
34	Montana	1,344
35	Wisconsin	1,256
36	Oklahoma	1,251
37	Minnesota	1,228
38	Wyoming	1,209
39	Virginia	1,201
40	Nebraska	1,180
41	North Carolina	1,157
42	Idaho	1,099
43	Colorado	1,072
44	New Hampshire	910
45	Washington	697
46	Puerto Rico	663
47	Utah	661
48	Oregon	568
49	Maine	552
50	Alaska	408
51	Vermont	363
52	Hawaii	324

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Oklahoma	8
2	West Virginia	7
3	Kentucky	6
4	Delaware	5
5	New Jersey	5
6	New York	4
7	Tennessee	4
8	Arkansas	3
9	District of Columbia	3
10	Florida	3
11	Georgia	3
12	Louisiana	3
13	Massachusetts	3
14	Mississippi	3
15	Rhode Island	3
16	Texas	3
17	Arizona	2
18	California	2
19	Kansas	2
20	Michigan	2
21	North Carolina	2
22	Ohio	2
23	Pennsylvania	2
24	Virginia	2
25	Wisconsin	2
26	Wyoming	2
27	Alabama	1
28	Connecticut	1
29	Idaho	1
30	Illinois	1
31	Iowa	1
32	Maine	1
33	Maryland	1
34	Minnesota	1
35	Missouri	1
36	Nevada	1
37	New Mexico	1
38	South Carolina	1
39	Vermont	1
40	Alaska	0
41	Colorado	0
42	Hawaii	0
43	Indiana	0
44	Montana	0
45	Nebraska	0
46	New Hampshire	0
47	North Dakota	0
48	Oregon	0
49	Puerto Rico	0
50	South Dakota	0
51	Utah	0
52	Washington	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	353,077	1	99
Chattahoochee	Georgia	318,969	2	99
Bent	Colorado	265,914	3	99
Dewey	South Dakota	244,399	4	99
Lincoln	Arkansas	244,011	5	99
Davidson	Tennessee	135,950	203	93
Richland	South Carolina	105,549	1026	67
York	South Carolina	101,858	1196	61
Orange	California	83,835	2035	35
Pierce	Washington	46,984	2902	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Gove	Kansas	8,346	2	99
Jerauld	South Dakota	7,948	3	99
Galax city	Virginia	7,878	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,494	1768	43
York	South Carolina	1,292	2018	35
Davidson	Tennessee	1,291	2019	35
Richland	South Carolina	1,268	2041	35
Pierce	Washington	670	2727	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.